Build AI Agents & RAG Apps with LangChain & CrewAI

Build real-world GenAI apps with RAG, LangChain, CrewAI, Hugging Face, Prompt Engineering and Python

Build real-world Generative AI applications using the latest tools like LangChain, RAG, AI Agents (CrewAI), and Hugging Face—all in one complete, hands-on course.

This course takes you from absolute setup to advanced AI systems, helping you understand not just how things work, but how to build production-ready AI applications.

Get Started from Scratch

Set up your development environment with ease:

• Install Anaconda, Jupyter Notebook, and VS Code

• Master Jupyter Notebook Markdown for clean workflows

• Enable GPU with CUDA, cuDNN, and PyTorch

Learn Python for AI (Beginner Friendly)

Build a strong foundation in Python:

• Variables, data types, and type conversion

• Control statements, loops, and functions

• Core data structures: lists, tuples, sets, dictionaries, strings

Understand AI, ML & Generative AI

• AI, Machine Learning, Deep Learning & Generative AI explained

• Evolution and history of AI

• Deep dive into Transformers & Attention Mechanism (Encoder–Decoder)

Master Foundation Models & Responsible AI

• What are Foundation Models and how they work

• Applications, types, and real-world examples

• Compare top open-source LLMs and choose the right model

• Learn Responsible AI practices and bias mitigation

Build LLM Apps with LangChain

• Chains, Agents, and Memory explained

• Build powerful LLM-driven applications step by step

Master RAG (Retrieval-Augmented Generation)

• End-to-end RAG pipeline:
  Input → Chunking → Embeddings → Vector DB → Retrieval → Response

• Build a complete Question-Answering system

• Work with vector databases:
  Pinecone, FAISS, Chroma, Weaviate, Milvus

Advanced Text Chunking Strategies

Learn and implement multiple chunking techniques:

• Character & Recursive Character Splitters

• Markdown Header Splitter

• Token-based Chunking

• Best practices for optimal RAG performance

Prompt Engineering Like a Pro

• Create and use OpenAI APIs

• Master prompting techniques:

  • Basic prompts

  • Role–Task–Context

  • Few-shot prompting

  • Chain-of-Thought

  • Constrained outputs

Work with Real Data

• Use document loaders: CSV, HTML, PDF

• Feed real-world data into your AI systems

Add Memory to LLMs

• Conversation Buffer Memory

• Window Memory

• Summary Memory

• Build AI that remembers context

Master LangChain Chains

• Single, Sequential & Router Chains

• Math Chain, SQL Chain, RAG Chain

• Build intelligent workflows with LLMs

Build Multi-Agent AI Systems (CrewAI)

• Understand Agentic AI frameworks

• Build real-world systems:

  • Web scraping agents

  • Email automation agents

  • Financial analysis agents

• Integrate LangChain tools with CrewAI

Build Apps with Hugging Face

• Use pretrained models for:

  • Text summarization

  • Translation

  • Sentence embeddings

  • Vision-based tasks (Image Q&A)

By the End of This Course, You Will:

• Build real-world GenAI applications from scratch

• Master RAG, LangChain, and AI Agents

• Work with industry tools used in AI engineering roles

• Be ready to create your own AI-powered products